class_docstr: Transformer-based forecaster that segments each time series into patches [Nie2023]_.
  
  Based on `gluonts.torch.model.d_linear.PatchTSTEstimator <https://ts.gluon.ai/stable/api/gluonts/gluonts.torch.model.patch_tst.html>`_.
  See GluonTS documentation for additional hyperparameters.
  
  References
  ----------
  .. [Nie2023] Nie, Yuqi, et al.
      "A Time Series is Worth 64 Words: Long-term Forecasting with Transformers."
      International Conference on Learning Representations. 2023.
  
  Other Parameters
  ----------------
  context_length : int, default = 96
      Number of time units that condition the predictions
  patch_len : int, default = 16
      Length of the patch.
  stride : int, default = 8
      Stride of the patch.
  d_model : int, default = 32
      Size of hidden layers in the Transformer encoder.
  nhead : int, default = 4
      Number of attention heads in the Transformer encoder which must divide d_model.
  num_encoder_layers : int, default = 2
      Number of layers in the Transformer encoder.
  distr_output : gluonts.torch.distributions.DistributionOutput, default = StudentTOutput()
      Distribution to fit.
  scaling : {"mean", "std", None}, default = "mean"
      Scaling applied to the inputs. One of ``"mean"`` (mean absolute scaling), ``"std"`` (standardization), ``None`` (no scaling).
  max_epochs : int, default = 100
      Number of epochs the model will be trained for
  batch_size : int, default = 64
      Size of batches used during training
  num_batches_per_epoch : int, default = 50
      Number of batches processed every epoch
  lr : float, default = 1e-3,
      Learning rate used during training
  weight_decay : float, default = 1e-8
      Weight decay regularization parameter.
code: def _get_estimator_class(self) -> Type[GluonTSEstimator]:
      from gluonts.torch.model.patch_tst import PatchTSTEstimator
  
      return PatchTSTEstimator
  
imports: import logging
  from typing import Any, Dict, Type
  from gluonts.model.estimator import Estimator
  from autogluon.timeseries.models.gluonts.abstract_gluonts import AbstractGluonTSModel
  from autogluon.timeseries.utils.datetime import get_lags_for_frequency, get_seasonality, get_time_features_for_frequency
label_desc: Use image node_id 2 for calling the PatchTSTModel obj's underlying member method code with example usage: obj._get_estimator_class() and returns: PatchTSTEstimator